Model Context Protocol (MCP) for Intelligent Data Analytics Description: In this project, I developed a smart backend architecture called MCP (Model Context Protocol) — a system designed to act as a bridge between natural language user queries and structured data from two databases: MongoDB and MySQL.

Lightweight, type-safe Haskell MCP server with extensible adapters and robust async I/O for flexible model-context workflows.

FastAPI-based agent scraping university data, retrieving official sites, deadlines, and CS rankings using LangChain and OpenAI.

WebSocket-based transport enabling efficient streaming LLM interactions with local models and extensible request handling for flexible workflows.

Exposes ESA.io data in MCP format with a CLI setup, endpoints, and Cloud Desktop-ready integration.

Guides building a Python MCP instance with modular tools, dynamic resources, and production deployment guidance via the MCP SDK.

Enables rich, bidirectional context sharing between IDA Pro and AI tooling with structured contexts and extensible adapters for enhanced reverse engineering workflows.

Model Context Protocol

Modular stdio transport toolkit for MCP, optimized for Cursor IDE, enabling pluggable, event-driven workflows across channels.

model context protocol

Bridges natural language prompts to conda operations, enabling env management, builds, and system insights.

mcp server

MCP server

Pilot MCP Servers

MCP Server Demo

MCP server

collection of mcp-servers

mcp server

MCP-Server

An MCP server that enables secure credential retrieval from 1Password to be used by Agentic AI

mcp_server_client_assessment

mcp

mcp

MCP Client for Cheat Engine API